from datasets.data_loader import (
    Dataset_ETT_hour,
    Dataset_ETT_minute,
    Dataset_Custom,
    Dataset_M4,
)
from torch.utils.data import DataLoader

data_dict = {
    "ETTh1": Dataset_ETT_hour,
    "ETTh2": Dataset_ETT_hour,
    "ETTm1": Dataset_ETT_minute,
    "ETTm2": Dataset_ETT_minute,
    "ECL": Dataset_Custom,
    "Traffic": Dataset_Custom,
    "Weather": Dataset_Custom,
    "m4": Dataset_M4,
}


def data_provider(args, flag):
    Data = data_dict[args.data]
    timeenc = 0 if args.embed != "timeF" else 1
    percent = args.percent

    if flag == "test":
        shuffle_flag = False
        drop_last = True
        batch_size = args.batch_size
        freq = args.freq
    else:
        shuffle_flag = True
        drop_last = True
        batch_size = args.batch_size
        freq = args.freq

    if args.data == "m4":
        drop_last = False
        data_set = Data(
            root_path=args.root_path,
            data_path=args.data_path,
            flag=flag,
            size=[args.seq_len, args.label_len, args.pred_len],
            features=args.features,
            target=args.target,
            timeenc=timeenc,
            freq=freq,
            seasonal_patterns=args.seasonal_patterns,
        )
    else:
        data_set = Data(
            root_path=args.root_path,
            data_path=args.data_path,
            flag=flag,
            size=[args.seq_len, args.label_len, args.pred_len],
            features=args.features,
            target=args.target,
            timeenc=timeenc,
            freq=freq,
            percent=percent,
            seasonal_patterns=args.seasonal_patterns,
        )
    data_loader = DataLoader(
        data_set,
        batch_size=batch_size,
        shuffle=shuffle_flag,
        num_workers=args.num_workers,
        drop_last=drop_last,
    )
    return data_set, data_loader
